What is Data Fabric?

Data Fabric Meaning

The definition of Data fabric is a software architecture for data management that unifies and integrates data across multiple systems. Data fabric uses a variety of approaches to create a unified data management system that allows organizations to access, process, and share data more efficiently.

Implementing a data fabric helps organizations store and manage data more efficiently and provide better services. Data fabrics are commonly used by companies that have large amounts of diverse types of data and use cases for their applications.

Data fabric integrates and manages multiple types of storage systems -- including file, object, block and cloud storage -- for unified access to structured and unstructured data.

Data fabrics are typically built on a distributed architecture based on nodes that are connected by high-speed networks, such as InfiniBand or Ethernet. The idea is to simplify the integration of disparate storage technologies into one cohesive system that works together to meet an enterprise's needs.

Key Pillars of Data Fabric Architecture

According to Gartner, the key pillars of data fabric architecture include collecting and analyzing all types of metadata, converting passive metadata to active metadata, creating knowledge graphs, and ensuring strong data integration. These elements enable integrated capabilities for data discovery, governance, curation, and orchestration, transforming complex data environments into efficient, automated systems for better data management.

1. Collect and Analyze All Types of Metadata

Metadata is the foundation of a data fabric architecture and includes technical metadata, operational metadata, and business metadata. Collecting and analyzing these different types of metadata enables organizations to gain a deeper understanding of their data. It facilitates data discovery, ensuring that teams can easily find and access the information they need. Additionally, it helps in identifying data quality issues, managing data lineage, and improving data security through traceability. 

• Technical Metadata refers to the structure, format, and location of data, providing insights into schemas, data models, and storage systems.
• Operational Metadata tracks the movement, transformation, and processing of data, offering visibility into data workflows, performance metrics, and lineage.
• Business Metadata includes business definitions, taxonomies, policies, and rules that give context to data, ensuring its relevance and alignment with organizational objectives.

2. Convert Passive Metadata to Active Metadata

Traditionally, metadata has been passive, serving merely as a descriptive layer providing information about data. In contrast, data fabric architecture transforms metadata into active metadata, which can interact dynamically with data systems to automate and enhance data management processes. Active metadata allows systems to perform actions based on the state and context of the data, enabling greater flexibility and responsiveness.

For example, when new data is ingested, active metadata can automatically classify and tag it with relevant business terms, updating data catalogs in real-time. It can also trigger adjustments to data pipelines when schema changes or performance issues arise. This dynamic approach improves data governance and automation, allowing organizations to manage their data ecosystems with greater agility. Active metadata ensures that data remains current, accurate, and aligned with business requirements without the need for manual intervention.

3. Create and Curate Knowledge Graphs

A key aspect of data fabric architecture is the use of knowledge graphs, which represent data in an interconnected and structured manner. Knowledge graphs build relationships between disparate data entities by utilizing metadata to map how data points are connected. These graphs serve as a semantic layer on top of the data, enabling more intuitive querying and discovery of relevant data.

Knowledge graphs provide several benefits:

• Data Discovery: Knowledge graphs make it easier to identify related data across different systems, breaking down data silos and promoting data reuse.
• Data Integration: By connecting data from different sources, knowledge graphs facilitate the integration of diverse datasets, regardless of their format or location.
• Advanced Analytics: The interconnected nature of knowledge graphs enables richer insights by allowing for complex queries, pattern recognition, and machine learning applications that consider relationships between data points.

4. Ensure a Robust Data Integration Foundation

A strong data integration foundation is crucial for the effective functioning of a data fabric architecture. Since data is often spread across multiple systems, clouds, and departments, integration is needed to unify and harmonize disparate data sources into a cohesive ecosystem.

Key components of a robust integration foundation include:

• ETL/ELT Pipelines: These pipelines ensure that data is efficiently extracted from various sources, transformed to meet organizational needs, and loaded into data repositories or analytical platforms. They are fundamental for creating a seamless flow of data across systems.
• Real-Time Data Streaming: In today’s data-driven world, many use cases require data to be processed and made available in real time. Real-time data streaming capabilities ensure that data can be accessed and acted upon immediately, especially for time-sensitive applications like predictive analytics or IoT data processing.

Data Virtualization: Data virtualization enables organizations to access and query data from multiple sources without physically moving it. This allows for faster decision-making, as users can access the data they need in real-time, regardless of where it is stored.

How to measure it?

Data fabric can be built on-premises or in the cloud. It allows organizations to move data where it needs to be accessed most quickly or cost-effectively. This allows users to access data in ways they haven't been able to before. Much of today's data is stored in the cloud rather than on premises. That makes it difficult for organizations to integrate all their data into one system for analysis. Data Fabric solves that problem by aggregating multiple sources of information from both on premises and cloud environments into a single unified platform.

The term "data fabric" is often used interchangeably with "hybrid cloud." Forrester Research defines hybrid cloud as a "cloud computing environment that uses a mix of on-premises, private cloud and third-party, public cloud services with orchestration between these platforms."

Data Fabric has a number of components. It's the new way to think about cloud computing, analytics and data management. The philosophy is that you can store data anywhere and use any technology to analyze it. Unlike traditional approaches, Data Fabric is easy to install, configure and use. It's also much less expensive than traditional approaches.

How is it different from a data mesh?

As defined above, data fabric is a method of connecting collections of data across several tools in a cohesive way. This means that ensuring data integrity and organization, as well as costs of hosting such data is all-inclusive. This is usually done with a single tool or platform that likely hosts these various data sources in the same place.

Data mesh, on the other hand, allows different teams to collect and store data as they see fit, even if that means doing so on separate platforms. However, they must follow certain guidelines and rules that have likely been laid out by data stewards within their organization, or were directed by their data governance council.

How is it different from data silos?

With the rise of the modern data stack along with the ever increasing number of tools for managing and working with data int he market, it can become really easy for data to become siloed. When this happens, there is little visibility or interaction between several sources of data, resulting in unreliable analytics and insights.

The best data fabric implementations solve this problem and result in data that is no longer siloed. This means that a good data fabric not only results in faster, cleaner data, but makes the data that is being accessed reliable and easy to understand.